A computer-aided diagnosis system for plus disease in retinopathy of prematurity with structure adaptive segmentation and vessel based features.

Retinopathy of Prematurity (ROP) is a blinding disease affecting the retina of low birth-weight preterm infants. Accurate diagnosis of ROP is essential to identify treatment-requiring ROP, which would help to prevent childhood blindness. Plus disease, which characterizes abnormal twisting, widening and branching of the blood vessels, is a significant symptom of treatment requiring ROP. In this paper, we have developed and evaluated a computer-based analysis system for objective assessment of plus disease in ROP, which best mimics the clinical method of disease diagnosis by identifying unique vessel based features. The proposed system consists of an initial segmentation stage, which will efficiently extract blood vessels of varying width and length by utilizing structure adaptive filtering, connectivity analysis and image fusion. The paper proposes the usage of additional retinal features namely leaf node count and vessel density, to portray the abnormal growth and branching of the blood vessels and to complement the commonly used features namely tortuosity and width. The test results show a better classification of plus disease in terms of sensitivity (95%) and specificity (93%), emphasizing the superiority of the proposed segmentation algorithm and vessel-based features. An additional advantage of the proposed system is that the process of selection of relevant vessels for feature extraction is fully automated, which makes the system highly useful to the non-physician graders, owing to the unavailability of a sufficient number of ROP specialists.